samee-r10 / resturant-data-analysis

Resturant data analysis using Python

Problem Statement:

An online e-commerce food aggregator wants to launch a new feature that provides data-driven insights and recommendations to restaurant owners. As part of this initiative, the company needs to analyze the available restaurant data to extract meaningful insights and provide actionable recommendations to improve the performance and competitiveness of the restaurants.

The objective of this analysis is to identify key factors that contribute to the success of restaurants and develop strategies to enhance their overall performance. The company wants to understand the relationship between various variables such as restaurant ratings, customer reviews, pricing, location, and competitor analysis.

Data columns:

• Restaurant ID : Unique id of every restaurant across various cities of the world
• Restaurant Name :Name of the restaurant
• Country code : Country in which restaurant is located
• City : City locations where the restaurant is located.
• Address : Address of the restaurant.
• Locality : Location in the city
• Locality verbose : Detailed description of the locality
• Longitude :Longitude coordinate of the restaurant's location.
• Latitude : Latitude coordinate of the restaurant’s location.
• Cuisines :Cuisines offered by the restaurant
• Average cost for two : Cost for two people in different currencies
• Currency : Currency of the country
• Has table booking : The restaurant allows pre-booking of the table or not.
• Has Online Delivery : The restaurant supports online food delivery or not.
• Switch to order menu: yes/no
• Price range: range of price of food
• Aggregate Rating: Average rating out of 5
• Rating color: depending upon the average rating color
• Rating text: text on the basis of rating of rating
• Votes: Number of ratings casted by people.

Objective:

To perform analysis & extract meaningful insights that can drive decision making and strategy formulation for new restaurant business setup.

Aspects to be covered:

1. Descriptive analytics : To make understand the restaurant's reviews & votes by providing the analysis.
2. Diagnostic analytics : To understand why the restaurant ratings are good or bad.
3. Predictive analytics : To make understand what is likely to happen in the new restaurant’s set up based on this data.
4. Prescriptive analytics : To understand how to proceed further or what factors to be considered if new restaurants needs to be setup.